ABSTRACT: Sugarcane (saccharum spp.) in Brazil is managed on the basis of “production environments”. These “production environments” are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the “production environments” in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new “production environments”, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.

Due to new possibilities for using sugarcane (Saccharum spp.) trash for electricity generation, and the production of 2nd generation ethanol and others chemicals, the interest for its recovery has increased. However, the question of how much trash can be removed from sugarcane field still needs to be clarified. This study evaluated the amount of dry matter, nutrients content, structural compounds and efficiency of the enzymatic hydrolysis of the hydrothermal pretreated materials for tops and dry leaves in samples from sugarcane varieties. Tops and dry leaves present differences in nutrients content and moisture. Therefore, the amount of trash to be collected should not be simply based on percentages, but also should take into account the different fractions of the crop residues. For instance, around 80 % of N, P and K were derived from tops. Therein, the environmental indicators of the entire chain of sugarcane could be benefited because more nutrients would be recycled and less mineral fertilizers might be used for sugarcane production if tops are left on the field. Further, the tops have seven times more moisture than dry leaves and higher amounts of extractives (organic compounds of low molecular weight). Moreover, as the result of yield obtained in the pretreatment steps for dry leaves were superior to the tops and the glucose yields obtained in the enzymatic hydrolysis step were similar, it can be predicted that for second generation ethanol production, it is more viable to recover parts of the dry leaves fraction, leaving the tops on the field.

O objetivo deste trabalho foi avaliar as correlações entre a produtividade da cana‑de‑açúcar e alguns atributos físicos e químicos do solo. Para isso, uma área de 42 ha, em Araras, SP, foi selecionada. As propriedades do solo foram determinadas a partir de amostras coletadas no início da safra 2003/2004, por meio de uma grade regular de 100x100 m. A produtividade foi avaliada por meio de um monitor de produtividade (Simprocana). Análises de correlação foram realizadas entre a produtividade da cana‑de‑açúcar e as seguintes propriedades do solo: pH, pH CaCl2, N, C, índice de cone, teor de argila, matéria orgânica, P, K, Ca, Mg, H + Al, capacidade de troca catiônica e saturação por bases. Os coeficientes de correlação foram respectivamente de ‑0,05, ‑0,29, 0,33, 0,41, ‑0,27, 0,22, 0,44, ‑0,24, traço, ‑0,06, 0,01, 0,32, 0,14 e 0,04. As correlações de atributos químicos e físicos do solo com a produtividade da cana‑de‑açúcar são baixas, por si mesmas, e não são capazes de explicar a variação na produtividade da cana‑de‑açúcar, o que indica que, além das propriedades do solo, outras variáveis devem ser analisadas.

The objective of this work was to evaluate the correlation between sugarcane yield and some physical and chemical attributes of soil. For this, a 42‑ha test area in Araras, SP, Brazil, was used. Soil properties were determined from samples collected at the beginning of the 2003/2004 harvest season, using a regular 100x100 m grid. Yield assessment was done with a yield monitor (Simprocana). Correlation analyses were performed between sugarcane yield and the following soil properties: pH, pH CaCl2, N, C, cone index, clay content, soil organic matter, P, K, Ca, Mg, H+AL, cation exchange capacity, and base saturation. Correlation coefficients were respectively ‑0.05, ‑0.29, 0.33, 0.41, ‑0.27, 0.22, 0.44, ‑0.24, trace, ‑0.06, 0.01, 0.32, 0.14, and 0.04. Correlations of chemical and physical attributes of soil with sugarcane yield are weak, and, per se, they are not able to explain sugarcane yield variation, which suggests that other variables, besides soil attributes, should be analysed.

Um dos desafios da agricultura de precisão é oferecer subsídios para a definição de unidades de manejo para posteriores intervenções. Portanto, o objetivo deste trabalho foi avaliar os atributos químicos do solo e a produtividade da cultura de cana-de-açúcar por meio da geoestatística e mineração de dados pela indução da árvore de decisão. A produtividade da cana-de-açúcar foi mapeada em uma área de aproximadamente 23ha, utilizando-se o critério de célula, por meio de um monitor de produtividade que permitiu a elaboração de um mapa digital que representa a superfície de produção para a área em estudo. Para determinar os atributos de um Argissolo Vermelho-Amarelo, foram coletadas as amostras no início da safra 2006/2007, utilizando-se uma grade regular de 50 x 50m, nas profundidades de 0,0-0,2m e 0,2-0,4m. Os dados dos atributos do solo e da produtividade foram analisados por meio da técnica de goestatística e classificados em três níveis de produção para indução de árvore de decisão. A árvore de decisão foi induzida no programa SAS Enterprise Miner, sendo utilizado algoritmo baseado na redução de entropia. As variáveis altitude e potássio apresentaram os maiores valores de correlação com a produtividade de cana-de-açúcar. A indução de árvores de decisão permitiu verificar que a altitude é a variável com maior potencial para interpretar os mapas de produtividade de cana-de-açúcar, auxiliando na agricultura de precisão e mostrando-se uma ferramenta adequada para o estudo de definição de zonas de manejo em área cultivada com essa cultura.

One of the challenges of precision agriculture is to offer subsidies for the definition of management units for posterior interventions. Therefore, the objective of this work was to evaluate soil chemical attributes and sugarcane yield with the use of geostatistics and data mining by decision tree induction. Sugarcane yield was mapped in a 23ha field, applying the cell criterion, by using a yield monitor that allowed the elaboration of a digital map representing the surface of production of the studied area. To determine the soil attributes, soil samples were collected at the beginning of the harvest in 2006/2007 using a regular grid of 50 x 50m, in the depths of 0.0-0.2m and 0.2-0.4m. Soil attributes and sugarcane yield data were analyzed by using geostatistics techniques and were classified into three yield levels for the elaboration of the decision tree. The decision tree was induced in the software SAS Enterprise Miner, using an algorithm based on entropy reduction. Altitude and potassium presented the highest values of correlation with sugarcane yield. The induction of decision trees showed that the altitude is the variable with the greatest potential to interpret the sugarcane yield maps, then assisting in precision agriculture and, revealing an adjusted tool for the study of management definition zones in area cropped with sugarcane.

O uso da geoestatística como técnica para identificação da estrutura espacial de vários fenômenos vem crescendo em aplicações agrícolas. Este trabalho apresenta um sistema computacional implementado em ambiente Windows (Borland Delphi), voltado à análise espacial de dados por meio de ferramentas, como estatísticas descritivas, modelagem de semivariogramas médios, direcionais e cruzados, auto-validação (Jack-Knifing) e krigagem. A fim de avaliar a acurácia dos resultados, o sistema foi testado por meio de um conjunto de dados de carbono e nitrogênio publicados em literatura. O sistema foi eficiente no processo de análise geoestatística para manipulação da rotina computacional num ambiente MS-DOS. A tentativa de desenvolvimento no Windows permitiu ao usuário modelar graficamente o semivariograma com maior grau de interação, sendo esta funcionalidade raramente disponível em programas similares. Devido a sua rápida prototipação e simplicidade após a incorporação de rotinas correlatas, o ambiente Delphi apresenta a principal vantagem de permitir a evolução do sistema.

Geostatistics identifies the spatial structure of variables representing several phenomena and its use is becoming more intense in agricultural activities. This paper describes a computer program, based on Windows Interfaces (Borland Delphi), which performs spatial analyses of datasets through geostatistic tools: Classical statistical calculations, average, cross- and directional semivariograms, simple kriging estimates and jackknifing calculations. A published dataset of soil Carbon and Nitrogen was used to validate the system. The system was useful for the geostatistical analysis process, for the manipulation of the computational routines in a MS-DOS environment. The Windows development approach allowed the user to model the semivariogram graphically with a major degree of interaction, functionality rarely available in similar programs. Given its characteristic of quick prototypation and simplicity when incorporating correlated routines, the Delphi environment presents the main advantage of permitting the evolution of this system.

Desenvolveu-se este trabalho com o objetivo de avaliar o desempenho de um arado de três discos, reversível, e estabelecer uma relação em função da velocidade de deslocamento e do ângulo de ataque horizontal dos discos, caracterizados pela resistência específica e pelo consumo de combustível por volume de solo mobilizado. Os resultados mostraram que a resistência específica do solo não diferiu significativamente nem com a velocidade de deslocamento nem com o ângulo de ataque horizontal dos discos. O consumo de combustível variou com a velocidade, mas não em relação ao ângulo de ataque horizontal dos discos. Os valores da resistência específica variaram na faixa de 6,37 a 7,22 N/cm², em média, enquanto o consumo de combustível por volume de solo mobilizado variou entre 6,17 e 9,05 mL/m³, em média, trabalhando a uma profundidade média de 20 cm, e velocidades de deslocamento de 2,5 a 6,0 km/h, em média.

This study was conducted with the objective of evaluating a disc plough and to establish a relationship between the soil specific resistance and fuel consumption by volume of soil disturbed established as function of the travel speed and the disc horizontal cutting angle. The results showed that the soil specific resistance did not differ significantly with the increment of the travel speed, and neither by alterations in the disc cutting angle. The fuel consumption by volume of soil disturbed reduced with the increment of travel speed but was not influenced by the disk cutting angle. The values of soil specific resistance was in a range of 6.37 to 7.22 N/cm², in average, and the fuel consumption by volume of soil disturbed was in a range of 6.17 to 9.05 mL/m³, in average, at the cutting depth of 20 cm. The tractor speed was in a range of 2.5 to 6.0 km/h, in average.